1. Field of the Invention
The present invention relates to a method for detecting a false alarm. More particularly, the present invention relates to a method for detecting a false alarm, which can detect a false alarm through a statistical analysis between pre-stored past data and currently measured data.
2. Description of the Prior Art
An anomaly detection system is a system which detects abnormality through monitoring of a processing state, the quality of a processed product, and the condition of equipment, and intercepts dangerous elements in advance.
As the most representatively utilized technique, a control chart is a technique which detects an inferiority phenomenon in early stages through real time monitoring of processing elements, and takes an appropriate measure so as to continue a normal management of the processing.
One of the largest problems of such existing statistical hypothesis test based methodologies is that they are vulnerable to a false alarm. Here, the false alarm means that an alarm is generated although the processing is in a normal state.
Frequently generated false alarms may cause inconvenience to users of the anomaly detection system, and increase management costs at a production spot to finally deteriorate reliability of the anomaly detection system itself.
The false alarm may be generated {circle around (1)}due to the problem of management limit setting that is caused by the fact that actual data does not follow a normal distribution although the anomaly detection system is designed on the assumption of such a normal distribution, or {circle around (2)}due to the limit of monitoring statistic that is unable to properly consider the characteristics of measured values that are changed in various forms, such as data nonlinearity, temporal variability, multi-normality, and multi-abnormality.
Accordingly, there is a need for a method capable of improving monitoring accuracy through alarm feedback learning in the anomaly detection field.